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Putting perception into action with inverse optimal control for continuous psychophysics.

Dominik Straub1,2, Constantin A Rothkopf1,2,3

  • 1Centre for Cognitive Science, Technical University of Darmstadt, Darmstadt, Germany.

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|September 29, 2022
PubMed
Summary

Continuous psychophysics offers a new way to study perception by analyzing continuous movements instead of simple responses. Our Bayesian framework accurately estimates perceptual thresholds and action variability, advancing cognitive science research.

Keywords:
continuous psychophysicshumaninverse reinforcement learningneuroscienceoptimal controlperception and actionrational analysis

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Area of Science:

  • Psychophysics
  • Cognitive Science
  • Neuroscience

Background:

  • Psychophysical methods quantify behavior and neural correlates in psychology, cognitive science, and neuroscience.
  • Traditional methods require many trials and trained participants, limiting their widespread adoption.
  • Continuous psychophysics offers an alternative with dynamic stimuli and continuous behavioral adjustments, but current analyses are limited.

Purpose of the Study:

  • To introduce a novel computational analysis framework for continuous psychophysics.
  • To address limitations in current analysis methods that inflate perceptual thresholds.
  • To enable more accurate estimation of perceptual thresholds and behavioral parameters.

Main Methods:

  • Developed a computational framework based on Bayesian inverse optimal control.
  • Applied the framework to simulations and previously published data.
  • Analyzed continuous behavioral adjustments to dynamic stimuli.

Main Results:

  • The framework accurately recovers perceptual thresholds.
  • Successfully estimates subjects' action variability and internal behavioral costs.
  • Provides insights into subjective beliefs about experimental stimulus dynamics.

Conclusions:

  • The Bayesian framework enhances the analysis of continuous psychophysics data.
  • Accurate estimation of perceptual thresholds is achievable with this new approach.
  • Highlights the importance of considering action uncertainties, beliefs, and behavioral costs in perception research.